ArticlePloS one2012
PROTS-RF: a robust model for predicting mutation-induced protein stability changes.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- NbBayesLM: bayesian prediction of nanobody thermostability using protein language model.Frontiers in bioinformatics · 2026Article
- DDGWizard: Integration of feature calculation resources for analysis and prediction of changes in protein thermostability upon point mutations.PLoS computational biology · 2025Article
- Machine-learning-guided Directed Evolution for AAV Capsid Engineering.Current pharmaceutical design · 2024Review
- FireProt 2.0: web-based platform for the fully automated design of thermostable proteins.Briefings in bioinformatics · 2023Article
- Machine Learning for Protein Engineering.ArXiv · 2023Article
- Using machine learning to predict the effects and consequences of mutations in proteins.Current opinion in structural biology · 2023Review
- The role of data imbalance bias in the prediction of protein stability change upon mutation.PloS one · 2023Article
- Turning Failures into Applications: The Problem of Protein ΔΔG Prediction.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Performance of Web tools for predicting changes in protein stability caused by mutations.BMC bioinformatics · 2021Article
- A critical review of five machine learning-based algorithms for predicting protein stability changes upon mutation.Briefings in bioinformatics · 2020Review
- Limitations and challenges in protein stability prediction upon genome variations: towards future applications in precision medicine.Computational and structural biotechnology journal · 2020Review
- Prediction of impacts of mutations on protein structure and interactions: SDM, a statistical approach, and mCSM, using machine learning.Protein science : a publication of the Protein Society · 2020Article
- Robust Prediction of Single and Multiple Point Protein Mutations Stability Changes.Biomolecules · 2019Article
- ProTstab - predictor for cellular protein stability.BMC genomics · 2019Article
- Machine-learning-guided directed evolution for protein engineering.Nature methods · 2019Review
- PON-tstab: Protein Variant Stability Predictor. Importance of Training Data Quality.International journal of molecular sciences · 2018Article
- Predicting the Effect of Single and Multiple Mutations on Protein Structural Stability.Molecules (Basel, Switzerland) · 2018Article
- Multifactorial level of extremostability of proteins: can they be exploited for protein engineering?Extremophiles : life under extreme conditions · 2017Review
- Structure Based Thermostability Prediction Models for Protein Single Point Mutations with Machine Learning Tools.PloS one · 2015Article
- Random forest-based protein model quality assessment (RFMQA) using structural features and potential energy terms.PloS one · 2014Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The ability to improve protein thermostability via protein engineering is of great scientific interest and also has significant practical value. In this report we present PROTS-RF, a robust model based on the Random Forest algorithm capable of predicting thermostability changes induced by not only single-, but also double- or multiple-point mutations. The model is built using 41 features including evolutionary information, secondary structure, solvent accessibility and a set of fragment-based features. It achieves accuracies of 0.799,0.782, 0.787, and areas under receiver operating characteristic (ROC) curves of 0.873, 0.868 and 0.862 for single-, double- and multiple- point mutation datasets, respectively. Contrary to previous suggestions, our results clearly demonstrate that a robust predictive model trained for predicting single point mutation induced thermostability changes can be capable of predicting double and multiple point mutations. It also shows high levels of robustness in the tests using hypothetical reverse mutations. We demonstrate that testing datasets created based on physical principles can be highly useful for testing the robustness of predictive models.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.